Assessment of genomic prediction accuracy using different selection and evaluation approaches in a simulated Korean beef cattle population.

IF 2.2 2区农林科学

Asian-Australasian Journal of Animal Sciences Pub Date : 2020-12-01 Epub Date: 2020-06-24 DOI:10.5713/ajas.20.0217

Chiemela Peter Nwogwugwu, Yeongkuk Kim, Hyunji Choi, Jun Heon Lee, Seung-Hwan Lee

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引用次数: 0

Abstract

Objective: This study assessed genomic prediction accuracies based on different selection methods, evaluation procedures, training population (TP) sizes, heritability (h2) levels, marker densities and pedigree error (PE) rates in a simulated Korean beef cattle population.

Methods: A simulation was performed using two different selection methods, phenotypic and estimated breeding value (EBV), with an h2 of 0.1, 0.3, or 0.5 and marker densities of 10, 50, or 777K. A total of 275 males and 2,475 females were randomly selected from the last generation to simulate ten recent generations. The simulation of the PE dataset was modified using only the EBV method of selection with a marker density of 50K and a heritability of 0.3. The proportions of errors substituted were 10%, 20%, 30%, and 40%, respectively. Genetic evaluations were performed using genomic best linear unbiased prediction (GBLUP) and single-step GBLUP (ssGBLUP) with different weighted values. The accuracies of the predictions were determined.

Results: Compared with phenotypic selection, the results revealed that the prediction accuracies obtained using GBLUP and ssGBLUP increased across heritability levels and TP sizes during EBV selection. However, an increase in the marker density did not yield higher accuracy in either method except when the h2 was 0.3 under the EBV selection method. Based on EBV selection with a heritability of 0.1 and a marker density of 10K, GBLUP and ssGBLUP_0.95 prediction accuracy was higher than that obtained by phenotypic selection. The prediction accuracies from ssGBLUP_0.95 outperformed those from the GBLUP method across all scenarios. When errors were introduced into the pedigree dataset, the prediction accuracies were only minimally influenced across all scenarios.

Conclusion: Our study suggests that the use of ssGBLUP_0.95, EBV selection, and low marker density could help improve genetic gains in beef cattle.

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在模拟的韩国肉牛群体中使用不同的选择和评估方法评估基因组预测的准确性。

目的：本研究在模拟的韩国肉牛群体中评估了基于不同选择方法、评估程序、训练群体（TP）大小、遗传率（h2）水平、标记密度和血统误差（PE）率的基因组预测准确性：采用表型和估计育种值（EBV）两种不同的选择方法进行模拟，h2 分别为 0.1、0.3 或 0.5，标记密度分别为 10、50 或 777K。从上一代中随机选取 275 个雄性和 2475 个雌性，模拟最近的十代。对 PE 数据集的模拟进行了修改，仅使用 EBV 选择法，标记密度为 50K，遗传率为 0.3。误差替换比例分别为 10%、20%、30% 和 40%。遗传评估采用基因组最佳线性无偏预测（GBLUP）和单步 GBLUP（ssGBLUP），并使用不同的加权值。测定了预测的准确性：结果表明：与表型选择相比，在 EBV 选择过程中，使用 GBLUP 和 ssGBLUP 获得的预测准确率在不同遗传力水平和 TP 大小下均有所提高。然而，除了在 EBV 选择法中 h2 为 0.3 时，标记密度的增加并没有提高两种方法的准确性。在遗传率为 0.1 和标记密度为 10K 的 EBV 选择条件下，GBLUP 和 ssGBLUP_0.95 的预测准确率高于表型选择。在所有情况下，ssGBLUP_0.95 的预测准确率都高于 GBLUP 方法。当在血统数据集中引入误差时，预测准确率在所有情况下都只受到很小的影响：我们的研究表明，使用 ssGBLUP_0.95、EBV 选择和低标记密度有助于提高肉牛的遗传收益。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Asian-Australasian Journal of Animal Sciences AGRICULTURE, DAIRY & ANIMAL SCIENCE-

自引率

0.00%

发文量

审稿时长

3 months

期刊介绍： Asian-Australasian Journal of Animal Sciences (AJAS) aims to publish original and cutting-edge research results and reviews on animal-related aspects of the life sciences. Emphasis will be placed on studies involving farm animals such as cattle, buffaloes, sheep, goats, pigs, horses, and poultry. Studies for the improvement of human health using animal models may also be publishable. AJAS will encompass all areas of animal production and fundamental aspects of animal sciences: breeding and genetics, reproduction and physiology, nutrition, meat and milk science, biotechnology, behavior, welfare, health, and livestock farming systems.